Time Series Modeling Essentials

Target group

Course structure

Introduction to Time Series

• Defining a time series.
• Using the TIMESERIES procedure to transform transactional data into time series data.
• Defining and exploring the systematic components in a time series.
• Describing the decomposition of time series variation.
• Listing three families of time series models.
• Introducing SAS Studio.
• Introducing the concepts of white noise and autocorrelation.

Exponential Smoothing Models

• Exploring weighted average models and exponential smoothing.
• Comparing and contrasting simple mean, random walk, and exponential smoothing models.
• Imputing missing values within a time series.

ARIMAX Models

• Differentiating between ARMA and ARIMA models.
• Defining a stationary time series and identifying its importance.
• Describing and identifying autoregressive and moving average processes.
• Defining the differences between a random walk series, a white noise series, and an autoregressive (AR) series.
• Estimating autoregressive parameters .
• ARMAX and time series regression.
• Accuracy and forecasting of ARIMAX.

Unobserved Components Models

• Introducing unobserved components models (UCM) and focus on the multiple sources of error and parameters as a function of time.
• Describing the basic component models: level, slope, seasonal.
• Exploring the UCM model parameters.
• Running a UCM model using the UCM procedure.
• Defining Random Walk and Linear Trend series.
• Building a UCM model.